Volume 25, Issue 2 (1-2007)                   jame 2007, 25(2): 1-10 | Back to browse issues page

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Abstract:   (5250 Views)
The theory of distributed detection is receiving a lot of attention. A common assumption used in previous studies is the conditional independence of the observations. In this paper, the optimization of local decision rules for distributed detection networks with correlated observations is considered. We focus on presenting the detection theory for parallel distributed detection networks with fixed fusion rules to develop a numeric algorithm based on Neyman-Pearson criterion. Simulation results are presented to demonstrate the efficiency and convergence properties of the algorithm.
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Type of Study: Research | Subject: General
Received: 2014/10/25 | Published: 2007/01/15

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